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North American Chapter of the Association for Computational Linguistics
Retrieval-Augmented Large Language Models (LLMs), which incorporate the non-parametric knowledge from external knowledge bases into LLMs, have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA)
Soyeong Jeong +4 more
semanticscholar +1 more source
Retrieval-Augmented Large Language Models (LLMs), which incorporate the non-parametric knowledge from external knowledge bases into LLMs, have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA)
Soyeong Jeong +4 more
semanticscholar +1 more source
Algorithms and Complexity in Durham
The P vs. NP problem is one of the fundamental problems of mathematics. It asks whether propositional tautologies can be recognized by a polynomial-time algorithm. The problem would be solved in the negative if one could show that there are propositional
J. Krajícek
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The P vs. NP problem is one of the fundamental problems of mathematics. It asks whether propositional tautologies can be recognized by a polynomial-time algorithm. The problem would be solved in the negative if one could show that there are propositional
J. Krajícek
semanticscholar +1 more source
Computational-Statistical Gaps in Gaussian Single-Index Models
Annual Conference Computational Learning TheorySingle-Index Models are high-dimensional regression problems with planted structure, whereby labels depend on an unknown one-dimensional projection of the input via a generic, non-linear, and potentially non-deterministic transformation.
Alex Damian +3 more
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Annual Conference Computational Learning Theory
We study the computational and sample complexity of learning a target function $f_*:\mathbb{R}^d\to\mathbb{R}$ with additive structure, that is, $f_*(x) = \frac{1}{\sqrt{M}}\sum_{m=1}^M f_m(\langle x, v_m\rangle)$, where $f_1,f_2,...,f_M:\mathbb{R}\to ...
Kazusato Oko +3 more
semanticscholar +1 more source
We study the computational and sample complexity of learning a target function $f_*:\mathbb{R}^d\to\mathbb{R}$ with additive structure, that is, $f_*(x) = \frac{1}{\sqrt{M}}\sum_{m=1}^M f_m(\langle x, v_m\rangle)$, where $f_1,f_2,...,f_M:\mathbb{R}\to ...
Kazusato Oko +3 more
semanticscholar +1 more source
The 1982 ACM Turing Award Lecture: An Overview of Computational Complexity
Logic, Automata, and Computational Complexity, 2023S. Cook
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On the Sample Complexity of the Linear Quadratic Regulator
Foundations of Computational Mathematics, 2017This paper addresses the optimal control problem known as the linear quadratic regulator in the case when the dynamics are unknown. We propose a multistage procedure, called Coarse-ID control, that estimates a model from a few experimental trials ...
Sarah Dean +4 more
semanticscholar +1 more source
Logic, Automata, and Computational Complexity: The Works of Stephen A. Cook
Logic, Automata, and Computational Complexity, 2023semanticscholar +1 more source
Randomized Complexity of Mean Computation and the Adaption Problem
Journal of ComplexityRecently the adaption problem of Information-Based Complexity (IBC) for linear problems in the randomized setting was solved in Heinrich (J. Complexity 82, 2024, 101821). Several papers treating further aspects of this problem followed.
S. Heinrich
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Current treatment and recent progress in gastric cancer
Ca-A Cancer Journal for Clinicians, 2021Smita S Joshi, Brian D Badgwell
exaly
A Complexity Trichotomy for k-Regular Asymmetric Spin Systems Using Number Theory
Computational Complexity, 2023Jin-Yi Cai +3 more
semanticscholar +1 more source